Self-adjusting parameter control for surrogate-assisted constrained optimization under limited budgets

Bagheri, Samineh; Konen, Wolfgang; Emmerich, Michael; Bäck, Thomas

doi:10.1016/j.asoc.2017.07.060

Cited by 58 publications

(49 citation statements)

References 28 publications

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“…If the functions are non-smooth or noisy, it is likely that GP surrogate degrades rapidly and overfits due to its interpolating behavior. A challenge for optimization under restricted budgets will be to find the right degree of approximation (smoothing factor) from a limited number of samples [ 102 ].…”

Section: Discussionmentioning

confidence: 99%

“…As Table 9 states, the proposed algorithm shows a significant improvement over GWO for θ = 0. In many studies on optimization, the strength of an optimization technique is measured by comparing the final solution achieved by different algorithms [101,102]. This approach only provides information about the quality of the results and neglects the speed of convergence which is a very important measure for expensive optimization problems.…”

Section: Plos Onementioning

confidence: 99%

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Robust optimal design of FOPID controller for five bar linkage robot in a Cyber-Physical System: A new simulation-optimization approach

et al. 2020

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This paper aims to further increase the reliability of optimal results by setting the simulation conditions to be as close as possible to the real or actual operation to create a Cyber-Physical System (CPS) view for the installation of the Fractional-Order PID (FOPID) controller. For this purpose, we consider two different sources of variability in such a CPS control model. The first source refers to the changeability of a target of the control model (multiple setpoints) because of environmental noise factors and the second source refers to an anomaly in sensors that is raised in a feedback loop. We develop a new approach to optimize two objective functions under uncertainty including signal energy control and response error control while obtaining the robustness among the source of variability with the lowest computational cost. A new hybrid surrogate-metaheuristic approach is developed using Particle Swarm Optimization (PSO) to update the Gaussian Process (GP) surrogate for a sequential improvement of the robust optimal result. The application of efficient global optimization is extended to estimate surrogate prediction error with less computational cost using a jackknife leave-one-out estimator. This paper examines the challenges of such a robust multi-objective optimization for FOPID control of a five-bar linkage robot manipulator. The results show the applicability and effectiveness of our proposed method in obtaining robustness and reliability in a CPS control system by tackling required computational efforts.

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Section: Discussionmentioning

confidence: 99%

Section: Plos Onementioning

confidence: 99%

Robust optimal design of FOPID controller for five bar linkage robot in a Cyber-Physical System: A new simulation-optimization approach

et al. 2020

View full text Add to dashboard Cite

show abstract

“…In many studies on optimization, the strength of an optimization technique is measured by comparing the final solution achieved by different algorithms. 93,94 This approach only provides information about the quality of the results and neglects the speed of convergence which is a very important measure for expensive optimization problems. Comparing the convergence curves (number of function evaluations) is also one of the common benchmarking approaches.…”

Section: Performance Measurementioning

confidence: 99%

Robust tuning and sensitivity analysis of stochastic integer andfractional‐order PIDcontrol systems: application ofsurrogate‐basedrobustsimulation‐optimization

Parnianifard

Fakhfakh

Kotti

et al. 2020

Int J Numerical Modelling

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This paper aims to make a trade‐off between performance and robustness in stochastic control systems with probabilistic uncertainties. For this purpose, we develop a surrogate‐based robust simulation‐optimization approach for robust tuning and analyzing the sensitivity of stochastic controllers. Kriging surrogate is combined with robust design optimization to construct a robust simulation‐optimization model in the class of dual response surfaces. Randomness in simulation experiments due to uncertainty is analyzed through bootstrapping technique by computing confidence regions for the estimation of Pareto frontier. Results confirmed a proper trade‐off between the model's performance with the measure of expected Integral Squared Error (ISE) and robustness against uncertainty in the plant's physical parameters. Finally, the proposed method is evaluated in terms of accuracy, computational cost, and simplicity particularly in comparison with some common existed techniques in the tuning of the Proportional‐Integral‐Derivative (PID) and Fractional‐Order PID (FOPID) controllers.

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“…One of the major disadvantages of parameter tuning, compared to parameter control, is the lack of flexibility and relevance, as population changes dynamically during the search process while parameters remain unchanged, preventing the population from being updated accurately. Contrary to parameter tuning, parameter control is far from being thoroughly researched [15]. The methods of parameter control have generally been classified into three categories since 1999 [8], including deterministic, adaptive and self-adaptive methods.…”

Section: Related Workmentioning

confidence: 99%

A Self-Adaptive Parameter Control Method Based on Entropy and Security Market Line for Scheduling Problems

Sun

2020

IEEE Access

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Scheduling problems, as one of the classic combinatorial optimization problems are essential issues in many fields. Various meta-heuristic algorithms have been adopted to solve scheduling problems. However, parameter control problem is still crucial to the performance of algorithms. In this paper, we propose a self-adaptive parameter control method based on entropy and security market line, fully considering the characteristics scheduling problems. It consists of two key parts: locus-entropy strategy and parameter-control strategy. Firstly, the entropy on each genetic locus is calculated to accurately evaluate the population status of scheduling algorithms. Then, a parameter-control strategy based on the conception of security market line is proposed to address the issue that the nature of multipeak in scheduling problems makes algorithms fall into local optimal solutions. The strategy maintains the solutions of good quality and eliminate the solutions of poor quality by using locus-entropy as feedback. Through our method, the balance between exploitation and exploration is kept in algorithms to perform well in scheduling problems with different dimensions and characteristics. These strategies are tightly linked to adjust parameters adaptively without introducing new parameters, so that meta-heuristic algorithms equipped with the proposed approach are able to find a better solution. Moreover, our parameter control method is universal for meta-heuristic algorithms. The proposed approach is hybrid with genetic algorithm and particle swarm optimization. The hybrid algorithms are first compared with the standard algorithms in multipeak benchmark functions, then with other variants of the standard algorithms in real-world single and multi-objective scheduling problems. The results demonstrate that the proposed approach is valid for different kinds of algorithms to enhance the performance of solving a variety of scheduling problems.

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Self-adjusting parameter control for surrogate-assisted constrained optimization under limited budgets

Cited by 58 publications

References 28 publications

Robust optimal design of FOPID controller for five bar linkage robot in a Cyber-Physical System: A new simulation-optimization approach

Robust optimal design of FOPID controller for five bar linkage robot in a Cyber-Physical System: A new simulation-optimization approach

Robust tuning and sensitivity analysis of stochastic integer andfractional‐order PIDcontrol systems: application ofsurrogate‐basedrobustsimulation‐optimization

A Self-Adaptive Parameter Control Method Based on Entropy and Security Market Line for Scheduling Problems

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